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Course Outline
Introduction to Energy-Efficient AI
- The importance of sustainability in AI
- An overview of energy usage in machine learning
- Case studies highlighting energy-efficient AI applications
Compact Model Architectures
- Grasping model size and complexity
- Strategies for designing small yet highly effective models
- Comparing various model architectures for efficiency
Optimization and Compression Techniques
- Model pruning and quantization methods
- Knowledge distillation for developing smaller models
- Efficient training approaches to lower energy consumption
Hardware Considerations for AI
- Choosing energy-efficient hardware for training and inference
- The function of specialized processors such as TPUs and FPGAs
- Achieving a balance between performance and power usage
Green Coding Practices
- Creating energy-efficient code
- Profiling and refining AI algorithms
- Best practices for sustainable software engineering
Renewable Energy and AI
- Incorporating renewable energy sources into AI operations
- Sustainability in data centers
- The future trajectory of green AI infrastructure
Lifecycle Assessment of AI Systems
- Calculating the carbon footprint of AI models
- Methods for reducing environmental impact across the AI lifecycle
- Case studies on lifecycle evaluation in AI
Policy and Regulation for Sustainable AI
- Navigating global standards and regulations
- The impact of policy on promoting energy-efficient AI
- Ethical implications and societal effects
Project and Assessment
- Building a prototype using small language models in a specific domain
- Presenting the energy-efficient AI system
- Assessment based on technical efficiency, innovation, and environmental benefit
Summary and Next Steps
Requirements
- A strong grasp of deep learning principles
- Proficiency in Python programming
- Practical experience with model optimization methods
Target Audience
- Machine learning engineers
- AI researchers and practitioners
- Sustainability advocates in the technology industry
21 Hours
